Precise and detailed predictions of fluid transport processes are required for a large number of applications. Fluid transport problems may arise in a wide variety of technical fields, such as determining pollutant dispersion in air or water, predicting renewable energy production capacity, and performing aquaculture management. Lagrangian particle tracking models represent a robust forecasting methodology that can be used for simulating a transport process within a fluid. Pursuant to Lagrangian particle tracking, pollutant mass is modeled using a set of particles. However, particle tracking accuracy is sensitive to a set of initial conditions including a user-specified number of particles, as well as a user-specified distribution for these particles. Properly configuring the Lagrangian particle tracking model requires substantial expertise and time-consuming trial-and-error simulation. Thus, there exists a need to overcome at least one of the preceding deficiencies and limitations of the related art.